For service and diagnostic purposes, machines are sometimes equipped with sensors for measuring operating conditions such as engine RPM, oil pressure, water temperature, boost pressure, oil contamination, electric motor current, hydraulic pressure, system voltage, and the like. In some cases, storage devices are provided to compile a data base for later evaluation of machine performance and to aid in diagnosis. Service personnel examine the accrued data to get a better picture of the causes of the failure or to aid in diagnosis. Similarly, service personnel can evaluate the stored data to predict future failures and to correct any problems before total component failure. In addition, these stored parameters may be examined by service or supervisory personnel to evaluate machine and/or operator performance to ensure maximum productivity of the machine. These issues are particularly pertinent to over-the-highway trucks and large work machines such as off-highway mining trucks, hydraulic excavators, track-type tractors, wheel loaders, and the like. These machines represent large capital investments and are capable of substantial productivity when operating. It is therefore important to predict failures so servicing can be scheduled during periods in which productivity will be less affected and so minor problems can be repaired before they lead to catastrophic failures.
Systems that have been used in the past to store all data produced by the machine sensors do not adequately address the needs of service personnel because such data is acquired while the machine is at substantially different operating conditions. For example, some of the data is acquired while the engine is idling while other of the data is acquired while the engine is under full load. Because of this, it is nearly impossible for service personnel to compare data acquired under such different circumstances and to observe any meaningful trends in the sensed parameters. This is a critical drawback for these systems since it is an examination of trends in the sensed parameters and comparisons between trends of multiple parameters that can be most useful during diagnosis and in predicting future failures.
Similarly, it is sometimes advantageous to accumulate parameters only when the machine is in a particular operating condition. This type of information is predominantly used during performance evaluation but may also be used in failure diagnosis and prognosis. For example, the length of time spent in a particular gear while the machine is loaded may be needed to evaluate machine performance. Without more, if service personnel can only look at a historical profile of each parameter, it is difficult to accurately determine the length of time the machine is operating in a particular gear while it is under load or in any other operating condition. Similarly, it is often desirable to provide information to supervisors regarding the length of time and fuel consumed while the machine is idling. To obtain such information would require service or supervisory personnel to carry out the burdensome task of manually calculating periods in which the engine is idling.
To further aid in diagnostics, it is beneficial to package information in such a way that analysis is simplified as much as possible. Since many sensed parameters are interrelated, service personnel often need to examine them together. Unfortunately, if data representing the parameters are stored separately, it is burdensome for service personnel to accurately and effectively study the interrelationship between the parameters. It would therefore be helpful to provide multidimensional histograms representing the interrelationship between multiple variables.
The present invention is directed to overcoming one or more of the problems set forth above.